Paper Citation Record · LEDGER

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks

As of 20 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2412.02924.

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pith.paper-citation-record.v1
2412.02924 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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External citation measurements

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Outbound references

Observation a7dcf74b-0168-4bd9-99ca-92cbf6a3e0fb · outbound

This paper cites Abadi et al.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Abadi et al

Reference 1

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Observation 488f9128-6d07-45dc-b2b5-8ca7ce1efe26 · outbound

This paper cites Computational fluid dynamics, volume 206.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Computational fluid dynamics, volume 206

Reference 2

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Observation a886c7b8-fdd3-4107-91b4-58c1af0dcf10 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 3

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This paper cites Enforcing analytic constraints in neural networks emulating physical systems.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Enforcing analytic constraints in neural networks emulating physical systems

Reference 4

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Observation 02bcb3ac-9203-4b0e-93b5-598364113fc3 · outbound

This paper cites Predicting waves in fluids with deep neural network.Physics of Fluids, 34(6), 2022.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Predicting waves in fluids with deep neural network.Physics of Fluids, 34(6), 2022

Reference 5

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Observation b57188af-1881-4d6e-8825-87835d3743a6 · outbound

This paper cites Combined space–time reduced-order model with three-dimensional deep convolution for extrapolating fluid dynamics.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Combined space–time reduced-order model with three-dimensional deep convolution for extrapolating fluid dynamics

Reference 6

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Observation a566d22f-e4bb-4397-b575-cbea61878132 · outbound

This paper cites Predicting wave propagation for varying bathymetry using conditional convolutional autoencoder network.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Predicting wave propagation for varying bathymetry using conditional convolutional autoencoder network

Reference 7

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Observation 75401299-ff70-40ad-9549-094546aab9fc · outbound

This paper cites Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net

Reference 8

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Observation 2a8bb1d4-5a36-4587-8165-004425d50074 · outbound

This paper cites A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations

Reference 9

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Observation fd06cb36-d3c2-4b94-8e7d-6797e281cba7 · outbound

This paper cites Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems

Reference 10

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Observation 0e9172b2-4b51-4492-8494-5c4b42ffcb3a · outbound

This paper cites Deep Learning.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Deep Learning

Reference 11

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Observation 58a64b77-098a-4831-872a-c33d6cbc62ff · outbound

This paper cites Griffiths.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Griffiths

Reference 12

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Observation 5653a3af-0439-47c1-9ebd-1c45a097e1ea · outbound

This paper cites Theory-guided data science: A new paradigm for scientific discovery from data.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Theory-guided data science: A new paradigm for scientific discovery from data

Reference 13

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Observation 38e59528-f2d8-4b00-ba9b-393765e0f4e0 · outbound

This paper cites Comparison of accurate methods for the integration of hyperbolic equations.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Comparison of accurate methods for the integration of hyperbolic equations

Reference 14

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Observation a650df15-c029-4ed8-a090-16bf6e67dcff · outbound

This paper cites Shape and time distortion loss for training deep time series forecasting models.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Shape and time distortion loss for training deep time series forecasting models

Reference 15

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This paper cites Deep learning.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Deep learning

Reference 16

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Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Unresolved cited work

Reference 17

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Observation 70a7ec65-abc4-4f10-91fd-a2391f803e8d · outbound

This paper cites A system for massively parallel hyperparameter tuning.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks A system for massively parallel hyperparameter tuning

Reference 18

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Observation 58d8dfbb-cce8-46ea-9970-8d08b18b59dc · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 19

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Observation cea2b797-cbab-44b6-bc1f-3e32640f7413 · outbound

This paper cites Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion mea- sures of softmax probabilities.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion mea- sures of softmax probabilities

Reference 20

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Observation e86701f4-2b46-46ba-8f14-db87d06f34d2 · outbound

This paper cites Reduced-order modeling for parameterized PDEs via implicit neural representations.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Reduced-order modeling for parameterized PDEs via implicit neural representations

Reference 21

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Observation 2336e2f0-53a2-4e50-97d8-77b8dcb91c0c · outbound

This paper cites Understanding adamw through proximal methods and scale-freeness.

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks Understanding adamw through proximal methods and scale-freeness

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:02:06.855906Z

Source-reported events for the cited work

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Pith citing papers

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